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<div class="section">
<div class="titlepage"><div><div>
<h2 class="title">
<a id="boosting-by-popularity"></a>Boosting by Popularity<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch-definitive-guide/edit/2.x/170_Relevance/45_Popularity.asciidoc">edit</a>
</h2>
</div></div></div>
<p>Imagine that we have a website that hosts blog posts and enables users to vote for the
blog posts that they like. We would like more-popular posts to appear higher in the
results list, but still have the full-text score as the main relevance driver.
We can do this easily by storing the number of votes with each blog post:</p>
<div class="pre_wrapper lang-json pagebreak-before">
<pre class="programlisting prettyprint lang-json pagebreak-before">PUT /blogposts/post/1
{
  "title":   "About popularity",
  "content": "In this post we will talk about...",
  "votes":   6
}</pre>
</div>
<p>At search time, we can use the <code class="literal">function_score</code> query with the
<code class="literal">field_value_factor</code> function to combine the number of votes with the full-text relevance score:</p>
<div class="pre_wrapper lang-json">
<pre class="programlisting prettyprint lang-json">GET /blogposts/post/_search
{
  "query": {
    "function_score": { <a id="CO112-1"></a><i class="conum" data-value="1"></i>
      "query": { <a id="CO112-2"></a><i class="conum" data-value="2"></i>
        "multi_match": {
          "query":    "popularity",
          "fields": [ "title", "content" ]
        }
      },
      "field_value_factor": { <a id="CO112-3"></a><i class="conum" data-value="3"></i>
        "field": "votes" <a id="CO112-4"></a><i class="conum" data-value="4"></i>
      }
    }
  }
}</pre>
</div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO112-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p>The <code class="literal">function_score</code> query wraps the main query and the function we would
like to apply.</p>
</td>
</tr>
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO112-2"><i class="conum" data-value="2"></i></a></p>
</td>
<td align="left" valign="top">
<p>The main query is executed first.</p>
</td>
</tr>
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO112-3"><i class="conum" data-value="3"></i></a></p>
</td>
<td align="left" valign="top">
<p>The <code class="literal">field_value_factor</code> function is applied to every document matching
the main <code class="literal">query</code>.</p>
</td>
</tr>
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO112-4"><i class="conum" data-value="4"></i></a></p>
</td>
<td align="left" valign="top">
<p>Every document <em>must</em> have a number in the <code class="literal">votes</code> field for
the <code class="literal">function_score</code> to work.</p>
</td>
</tr>
</table>
</div>
<p>In the preceding example, the final <code class="literal">_score</code> for each document has been altered as
follows:</p>
<pre class="literallayout">new_score = old_score * number_of_votes</pre>

<p>This will not give us great results.  The full-text <code class="literal">_score</code> range
usually falls somewhere between 0 and 10. As can be seen in <a class="xref" href="boosting-by-popularity.html#img-popularity-linear" title="Linear popularity based on an original _score of 2.0">Figure 29, “Linear popularity based on an original <code class="literal">_score</code> of <code class="literal">2.0</code>”</a>, a blog post with 10 votes will
completely swamp the effect of the full-text score, and a blog post with 0
votes will reset the score to zero.</p>
<div id="img-popularity-linear" class="imageblock">
<div class="content">
<img src="images/elas_1701.png" alt="Linear popularity based on an original `_score` of `2.0`">
</div>
<div class="title">Figure 29. Linear popularity based on an original <code class="literal">_score</code> of <code class="literal">2.0</code>
</div>
</div>
<div class="section">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="_modifier"></a>modifier<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch-definitive-guide/edit/2.x/170_Relevance/45_Popularity.asciidoc">edit</a>
</h3>
</div></div></div>
<p>A better way to incorporate popularity is to smooth out the <code class="literal">votes</code> value
with some <code class="literal">modifier</code>.  In other words, we want the first few votes to count a
lot, but for each subsequent vote to count less.  The difference between 0
votes and 1 vote should be much bigger than the difference between 10 votes
and 11 votes.</p>
<p>A typical <code class="literal">modifier</code> for this use case is <code class="literal">log1p</code>, which changes the formula
to the following:</p>
<pre class="literallayout">new_score = old_score * log(1 + number_of_votes)</pre>

<p>The <code class="literal">log</code> function smooths out the effect of the <code class="literal">votes</code> field to provide a
curve like the one in <a class="xref" href="boosting-by-popularity.html#img-popularity-log" title="Logarithmic popularity based on an original _score of 2.0">Figure 30, “Logarithmic popularity based on an original <code class="literal">_score</code> of <code class="literal">2.0</code>”</a>.</p>
<div id="img-popularity-log" class="imageblock">
<div class="content">
<img src="images/elas_1702.png" alt="Logarithmic popularity based on an original `_score` of `2.0`">
</div>
<div class="title">Figure 30. Logarithmic popularity based on an original <code class="literal">_score</code> of <code class="literal">2.0</code>
</div>
</div>
<p>The request with the <code class="literal">modifier</code> parameter looks like the following:</p>
<div class="pre_wrapper lang-json">
<pre class="programlisting prettyprint lang-json">GET /blogposts/post/_search
{
  "query": {
    "function_score": {
      "query": {
        "multi_match": {
          "query":    "popularity",
          "fields": [ "title", "content" ]
        }
      },
      "field_value_factor": {
        "field":    "votes",
        "modifier": "log1p" <a id="CO113-1"></a><i class="conum" data-value="1"></i>
      }
    }
  }
}</pre>
</div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO113-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p>Set the <code class="literal">modifier</code> to <code class="literal">log1p</code>.</p>
</td>
</tr>
</table>
</div>
<p class="pagebreak-before">The available modifiers are <code class="literal">none</code> (the default), <code class="literal">log</code>, <code class="literal">log1p</code>, <code class="literal">log2p</code>,
<code class="literal">ln</code>, <code class="literal">ln1p</code>, <code class="literal">ln2p</code>, <code class="literal">square</code>, <code class="literal">sqrt</code>,  and <code class="literal">reciprocal</code>.  You can read more
about them in the
<a href="/guide/en/elasticsearch/reference/2.4/query-dsl-function-score-query.html#function-field-value-factor" class="ulink" target="_top"><code class="literal">field_value_factor</code> documentation</a>.</p>
</div>

<div class="section">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="_factor"></a>factor<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch-definitive-guide/edit/2.x/170_Relevance/45_Popularity.asciidoc">edit</a>
</h3>
</div></div></div>
<p>The strength of the popularity effect can be increased or decreased by
multiplying the value in the <code class="literal">votes</code> field by some number, called the
<code class="literal">factor</code>:</p>
<div class="pre_wrapper lang-json">
<pre class="programlisting prettyprint lang-json">GET /blogposts/post/_search
{
  "query": {
    "function_score": {
      "query": {
        "multi_match": {
          "query":    "popularity",
          "fields": [ "title", "content" ]
        }
      },
      "field_value_factor": {
        "field":    "votes",
        "modifier": "log1p",
        "factor":   2 <a id="CO114-1"></a><i class="conum" data-value="1"></i>
      }
    }
  }
}</pre>
</div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO114-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p>Doubles the popularity effect</p>
</td>
</tr>
</table>
</div>
<p>Adding in a <code class="literal">factor</code> changes the formula to this:</p>
<pre class="literallayout">new_score = old_score * log(1 + factor * number_of_votes)</pre>

<p>A <code class="literal">factor</code> greater than <code class="literal">1</code> increases the effect, and a <code class="literal">factor</code> less than <code class="literal">1</code>
decreases the effect, as shown in <a class="xref" href="boosting-by-popularity.html#img-popularity-factor" title="Logarithmic popularity with different factors">Figure 31, “Logarithmic popularity with different factors”</a>.</p>
<div id="img-popularity-factor" class="imageblock">
<div class="content">
<img src="images/elas_1703.png" alt="Logarithmic popularity with different factors">
</div>
<div class="title">Figure 31. Logarithmic popularity with different factors</div>
</div>
</div>

<div class="section">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="_boost_mode"></a>boost_mode<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch-definitive-guide/edit/2.x/170_Relevance/45_Popularity.asciidoc">edit</a>
</h3>
</div></div></div>
<p>Perhaps multiplying the full-text score by the result of the
<code class="literal">field_value_factor</code> function still has too large an effect.  We can control
how the result of a function is combined with the <code class="literal">_score</code> from the query by
using the <code class="literal">boost_mode</code> parameter, which accepts the following values:</p>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">multiply</code>
</span>
</dt>
<dd>
Multiply the <code class="literal">_score</code> with the function result (default)
</dd>
<dt>
<span class="term">
<code class="literal">sum</code>
</span>
</dt>
<dd>
Add the function result to the <code class="literal">_score</code>
</dd>
<dt>
<span class="term">
<code class="literal">min</code>
</span>
</dt>
<dd>
The lower of the <code class="literal">_score</code> and the function result
</dd>
<dt>
<span class="term">
<code class="literal">max</code>
</span>
</dt>
<dd>
The higher of the <code class="literal">_score</code> and the function result
</dd>
<dt>
<span class="term">
<code class="literal">replace</code>
</span>
</dt>
<dd>
Replace the <code class="literal">_score</code> with the function result
</dd>
</dl>
</div>
<p>If, instead of multiplying, we add the function result to the <code class="literal">_score</code>, we can
achieve a much smaller effect, especially if we use a low <code class="literal">factor</code>:</p>
<div class="pre_wrapper lang-json">
<pre class="programlisting prettyprint lang-json">GET /blogposts/post/_search
{
  "query": {
    "function_score": {
      "query": {
        "multi_match": {
          "query":    "popularity",
          "fields": [ "title", "content" ]
        }
      },
      "field_value_factor": {
        "field":    "votes",
        "modifier": "log1p",
        "factor":   0.1
      },
      "boost_mode": "sum" <a id="CO115-1"></a><i class="conum" data-value="1"></i>
    }
  }
}</pre>
</div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO115-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p>Add the function result to the <code class="literal">_score</code>.</p>
</td>
</tr>
</table>
</div>
<p>The formula for the preceding request now looks like this (see <a class="xref" href="boosting-by-popularity.html#img-popularity-sum" title="Combining popularity with sum">Figure 32, “Combining popularity with <code class="literal">sum</code>”</a>):</p>
<pre class="literallayout">new_score = old_score + log(1 + 0.1 * number_of_votes)</pre>

<div id="img-popularity-sum" class="imageblock">
<div class="content">
<img src="images/elas_1704.png" alt="Combining popularity with `sum`">
</div>
<div class="title">Figure 32. Combining popularity with <code class="literal">sum</code>
</div>
</div>
</div>

<div class="section">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="_max_boost"></a>max_boost<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch-definitive-guide/edit/2.x/170_Relevance/45_Popularity.asciidoc">edit</a>
</h3>
</div></div></div>
<p>Finally, we can cap the maximum effect that the function can have by using the
<code class="literal">max_boost</code> parameter:</p>
<div class="pre_wrapper lang-json">
<pre class="programlisting prettyprint lang-json">GET /blogposts/post/_search
{
  "query": {
    "function_score": {
      "query": {
        "multi_match": {
          "query":    "popularity",
          "fields": [ "title", "content" ]
        }
      },
      "field_value_factor": {
        "field":    "votes",
        "modifier": "log1p",
        "factor":   0.1
      },
      "boost_mode": "sum",
      "max_boost":  1.5 <a id="CO116-1"></a><i class="conum" data-value="1"></i>
    }
  }
}</pre>
</div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO116-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p>Whatever the result of the <code class="literal">field_value_factor</code> function, it will never be
greater than <code class="literal">1.5</code>.</p>
</td>
</tr>
</table>
</div>
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<p>The <code class="literal">max_boost</code> applies a limit to the result of the function only, not
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